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22 results for “EEG decoding”
MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music
<p>The <em><strong>MAD-EEG Dataset</strong></em> is a research corpus for studying EEG-based auditory attention decoding to a target instrument in polyphonic music. </p> <p>The dataset consists of 20-channel EEG responses to music recorded from 8 subjects while attending to a particular instrument in a music mixture. </p> <p>For further details, please refer to the paper: <em><a href="https://hal.archives-ouvertes.fr/hal-02291882/document">MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music</a>.</em></p> <p>If you use the data in your research, please reference the paper (not just the Zenodo record):</p> <pre><code>@inproceedings{Cantisani2019, author={Giorgia Cantisani and Gabriel Trégoat and Slim Essid and Gaël Richard}, title={{MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music}}, year=2019, booktitle={Proc. SMM19, Workshop on Speech, Music and Mind 2019}, pages={51--55}, doi={10.21437/SMM.2019-11}, url={http://dx.doi.org/10.21437/SMM.2019-11} }</code></pre> <p> </p>
Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods
<p>EEG signals recorded from seven healthy subjects. On average, Seventy-three minutes of EEG data were recorded from 31 electrodes placed according to the extended 10-20 system. Signals are used in the paradigm-agnostic post-hoc labeled dataset generation framework for benchmarking of oscillatory neural decoding methods.</p>
EEG and audio dataset for auditory attention decoding
<p>This dataset contains EEG recordings from 18 subjects listening to one of two competing speech audio streams. Continuous speech in trials of ~50 sec. was presented to normal hearing listeners in simulated rooms with different degrees of reverberation. Subjects were asked to attend one of two spatially separated speakers (one male, one female) and ignore the other. Repeated trials with presentation of a single talker were also recorded. The data were recorded in a double-walled soundproof booth at the Technical University of Denmark (DTU) using a 64-channel Biosemi system and digitized at a sampling rate of 512 Hz. Full details can be found in:</p> <ul> <li><strong>Søren A. Fuglsang, Torsten Dau & Jens Hjortkjær (2017): Noise-robust cortical tracking of attended speech in real-life environments. <em>NeuroImage</em>, 156, 435-444</strong></li> </ul> <p>and</p> <ul> <li><strong>Daniel D.E. Wong, Søren A. Fuglsang, Jens Hjortkjær, Enea Ceolini, Malcolm Slaney & Alain de Cheveigné: A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding. Frontiers in Neuroscience, </strong><a href="https://doi.org/10.3389/fnins.2018.00531">https://doi.org/10.3389/fnins.2018.00531</a></li> </ul> <p>The data is organized in format of the publicly available <a href="https://zenodo.org/record/1198430">COCOHA Matlab Toolbox</a>. The preproc_script.m demonstrates how to import and align the EEG and audio data. The script also demonstrates some EEG preprocessing steps as used the Wong et al. paper above. The AUDIO.zip contains wav-files with the speech audio used in the experiment. The EEG.zip contains MAT-files with the EEG/EOG data for each subject. The EEG/EOG data are found in <strong>data.eeg</strong> with the following channels:</p> <ul> <li>channels 1-64: scalp EEG electrodes</li> <li>channel 65: right mastoid electrode</li> <li>channel 66: left mastoid electrode</li> <li>channel 67: vertical EOG below right eye</li> <li>channel 68: horizontal EOG right eye</li> <li>channel 69: vertical EOG above right eye</li> <li>channel 70: vertical EOG below left eye</li> <li>channel 71: horizontal EOG left eye</li> <li>channel 72: vertical EOG above left eye</li> </ul> <p>The <strong>expinfo</strong> table contains information about experimental conditions, including what what speaker the listener was attending to in different trials. The expinfo table contains the following information:</p> <ul> <li>attend_mf: attended speaker (1=male, 2=female)</li> <li>attend_lr: spatial position of the attended speaker (1=left, 2=right)</li> <li>acoustic_condition: type of acoustic room (1= anechoic, 2= mild reverberation, 3= high reverberation, see Fuglsang et al. for details)</li> <li>n_speakers: number of speakers presented (1 or 2)</li> <li>wavfile_male: name of presented audio wav-file for the male speaker</li> <li>wavfile_female: name of presented audio wav-file for the female speaker (if any)</li> <li>trigger: trigger event value for each trial also found in data.event.eeg.value</li> </ul> <p>DATA_preproc.zip contains the preprocessed EEG and audio data as output from preproc_script.m.</p> <p>The dataset was created within the <a href="https://cocoha.org/">COCOHA</a><a href="https://cocoha.org/"> Project</a>: Cognitive Control of a Hearing Aid</p>
Upper limb movements can be decoded from the time-domain of low-frequency EEG
<p>How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.</p>
Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury
<p>We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).</p>
EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.
<p>The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details.</p> <p>More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>.</p> <p>Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>.</p> <p>The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json.</p> <p>Please note some details about the stimulus presentation for the various listening conditions:</p> <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> <p>If you use this data, please cite the original publications, as well as this repository [1,2,3].</p> <p>[1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. <em>NeuroImage</em> <strong>200</strong> 1–11</p> <p>[2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. <em>J. Neurosci.</em> <strong>39</strong> 5750–9</p> <p>[3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208</p>
Video-EEG Encoding-Decoding Dataset KU Leuven
<p><strong> If using this dataset, please cite the following paper and the current Zenodo repository.</strong></p> <p>This dataset is described in detail in the following paper:</p> <p><a href="https://iopscience.iop.org/article/10.1088/1741-2552/ad2333/meta">[1] Yao, Y., Stebner, A., Tuytelaars, T., Geirnaert, S., & Bertrand, A. (2024). Identifying temporal correlations between natural single-shot videos and EEG signals. <em>Journal of Neural Engineering</em>, <em>21</em>(1), 016018. doi:10.1088/1741-2552/ad2333</a></p> <p>The associated code is available at: <a href="https://github.com/YYao-42/Identifying-Temporal-Correlations-Between-Natural-Single-shot-Videos-and-EEG-Signals?tab=readme-ov-file">https://github.com/YYao-42/Identifying-Temporal-Correlations-Between-Natural-Single-shot-Videos-and-EEG-Signals?tab=readme-ov-file</a></p> <h2><strong>Introduction</strong></h2> <p>The research work leading to this dataset was conducted at the Department of Electrical Engineering (ESAT), KU Leuven.</p> <p>This dataset contains electroencephalogram (EEG) data collected from 19 young participants with normal or corrected-to-normal eyesight when they were watching a series of carefully selected YouTube videos. The videos were muted to avoid the confounds introduced by audio. For synchronization, a square box was encoded outside of the original frames and flashed every 30 seconds in the top right corner of the screen. A photosensor, detecting the light changes from this flashing box, was affixed to that region using black tape to ensure that the box did not distract participants. The EEG data was recorded using a BioSemi ActiveTwo system at a sample rate of 2048 Hz. Participants wore a 64-channel EEG cap, and 4 electrooculogram (EOG) sensors were positioned around the eyes to track eye movements.</p> <p>The dataset includes a total of <strong>(19 subjects x 63 min + 9 subjects x 24 min)</strong> of data. Further details can be found in the following section.</p> <h2><strong>Content</strong></h2> <ul> <li>YouTube Videos: Due to copyright constraints, the dataset includes links to the original YouTube videos along with precise timestamps for the segments used in the experiments. The features proposed in [1] (<em>Object Flow</em>) have been extracted and can be downloaded here: <a href="https://drive.google.com/file/d/1J1tYrxVizrl1xP-W1imvlA_v-DPzZ2Qh/view?usp=sharing">https://drive.google.com/file/d/1J1tYrxVizrl1xP-W1imvlA_v-DPzZ2Qh/view?usp=sharing</a>.</li> <li>Raw EEG Data: Organized by subject ID, the dataset contains EEG segments corresponding to the presented videos. Both EEGLAB .set files (containing metadata) and .fdt files (containing raw data) are provided, which can also be read by popular EEG analysis Python packages such as MNE. <ul> <li>The naming convention links each EEG segment to its corresponding video. E.g., the EEG segment 01_eeg corresponds to video 01_Dance_1, 03_eeg corresponds to video 03_Acrob_1, Mr_eeg corresponds to video Mr_Bean, etc.</li> <li>The raw data have 68 channels. The first 64 channels are EEG data, and the last 4 channels are EOG data. The position coordinates of the standard BioSemi headcaps can be downloaded here: <a href="https://www.biosemi.com/download/Cap_coords_all.xls">https://www.biosemi.com/download/Cap_coords_all.xls</a>.</li> <li>Due to minor synchronization ambiguities, different clocks in the PC and EEG recorder, and missing or extra video frames during video playback (rarely occurred), the length of the EEG data may not perfectly match the corresponding video data. The difference, typically within a few milliseconds, can be resolved by truncating the modality with the excess samples.</li> </ul> </li> <li>Signal Quality Information: A supplementary .txt file detailing potential bad channels. Users can opt to create their own criteria for identifying and handling bad channels.</li> </ul> <p>The dataset is divided into two subsets: Single-shot and MrBean, based on the characteristics of the video stimuli.</p> <h3><strong>Single-shot Dataset</strong></h3> <p>The stimuli of this dataset consist of 13 single-shot videos (63 min in total), each depicting a single individual engaging in various activities such as dancing, mime, acrobatics, and magic shows. All the participants watched this video collection.</p> <table> <tbody> <tr> <th>Video ID</th> <th>Link</th> <th>Start time (s)</th> <th>End time (s)</th> </tr> </tbody> <tbody> <tr> <td>01_Dance_1</td> <td><a href="https://youtu.be/uOUVE5rGmhM">https://youtu.be/uOUVE5rGmhM</a></td> <td>8.54</td> <td>231.20</td> </tr> <tr> <td>03_Acrob_1</td> <td><a href="https://youtu.be/DjihbYg6F2Y">https://youtu.be/DjihbYg6F2Y</a></td> <td>4.24</td> <td>231.91</td> </tr> <tr> <td>04_Magic_1</td> <td><a href="https://youtu.be/CvzMqIQLiXE">https://youtu.be/CvzMqIQLiXE</a></td> <td>3.68</td> <td>348.17</td> </tr> <tr> <td>05_Dance_2</td> <td><a href="https://youtu.be/f4DZp0OEkK4">https://youtu.be/f4DZp0OEkK4</a></td> <td>5.05</td> <td>227.99</td> </tr> <tr> <td>06_Mime_2</td> <td><a href="https://youtu.be/u9wJUTnBdrs">https://youtu.be/u9wJUTnBdrs</a></td> <td>5.79</td> <td>347.05</td> </tr> <tr> <td>07_Acrob_2</td> <td><a href="https://youtu.be/kRqdxGPLajs">https://youtu.be/kRqdxGPLajs</a></td> <td>183.61</td> <td>519.27</td> </tr> <tr> <td>08_Magic_2</td> <td><a href="https://youtu.be/FUv-Q6EgEFI">https://youtu.be/FUv-Q6EgEFI</a></td> <td>3.36</td> <td>270.62</td> </tr> <tr> <td>09_Dance_3</td> <td><a href="https://youtu.be/LXO-jKksQkM">https://youtu.be/LXO-jKksQkM</a></td> <td>5.61</td> <td>294.17</td> </tr> <tr> <td>12_Magic_3</td> <td><a href="https://youtu.be/S84AoWdTq3E">https://youtu.be/S84AoWdTq3E</a></td> <td>1.76</td> <td>426.36</td> </tr> <tr> <td>13_Dance_4</td> <td><a href="https://youtu.be/0wc60tA1klw">https://youtu.be/0wc60tA1klw</a></td> <td>14.28</td> <td>217.18</td> </tr> <tr> <td>14_Mime_3</td> <td><a href="https://youtu.be/0Ala3ypPM3M">https://youtu.be/0Ala3ypPM3M</a></td> <td>21.87</td> <td>386.84</td> </tr> <tr> <td>15_Dance_5</td> <td><a href="https://youtu.be/mg6-SnUl0A0">https://youtu.be/mg6-SnUl0A0</a></td> <td>15.14</td> <td>233.85</td> </tr> <tr> <td>16_Mime_6</td> <td><a href="https://youtu.be/8V7rhAJF6Gc">https://youtu.be/8V7rhAJF6Gc</a></td> <td>31.64</td> <td>388.61</td> </tr> </tbody> </table> <h3><strong>MrBean Dataset</strong></h3> <p>Additionally, 9 participants watched an extra 24-minute clip from the first episode of Mr. Bean, where multiple (moving) objects may exist and interact, and the camera viewpoint may change. The subject IDs and the signal quality files are inherited from the single-shot dataset.</p> <table> <tbody> <tr> <th>Video ID</th> <th>Link</th> <th>Start time (s)</th> <th>End time (s)</th> </tr> </tbody> <tbody> <tr> <td>Mr_Bean</td> <td><a href="https://www.youtube.com/watch?v=7Im2I6STbms">https://www.youtube.com/watch?v=7Im2I6STbms</a></td> <td>39.77</td> <td>1495.00</td> </tr> </tbody> </table> <h2><strong>Acknowledgement</strong></h2> <p>This research is funded by the Research Foundation - Flanders (FWO) project No G081722N, junior postdoctoral fellowship fundamental research of the FWO (for S. Geirnaert, No. 1242524N), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 802895), the Flemish Government (AI Research Program), and the PDM mandate from KU Leuven (for S. Geirnaert, No PDMT1/22/009).</p> <p>We also thank the participants for their time and effort in the experiments.</p> <h2><strong>Contact Information</strong></h2> <p>Executive researcher: Yuanyuan Yao, <a href="mailto:yuanyuan.yao@kuleuven.be">yuanyuan.yao@kuleuven.be</a></p> <p>Led by: Prof. Alexander Bertrand, <a href="mailto:alexander.bertrand@kuleuven.be">alexander.bertrand@kuleuven.be</a></p> <p> </p>
Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding
<p>This is the data and script that reproduces the results of the paper:<br> Kato et al., PNAS 2022, Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding.<br> As the results of each analysis that are needed for other analysis are also contained in this folder, <br> each sub-section of the script can be run independently (e.g., you can plot OERP waveforms or GFPs without executing preprocessing). <br> Refer to README.txt for detailed discription.</p>
Imperial College Ear-EEG Dataset for Auditory Attention Decoding
<p>This repository contains the ear-EEG data and the speech material that were used in our publication "Decoding of Selective Attention to Speech From Ear-EEG Recordings" [1].</p> <p>The data are stored in the archive "icl_earEEG_dataset.zip", which contains:</p> <ul> <li>audio/ - this directory contains the original speech material from each EEG trial.</li> <li>eeg_data.h5: this file contains the unprocessed EEG recordings from each trial, sampled at the original rate of 256 Hz.</li> </ul> <p>The onsets of the EEG and speech material are already aligned - all you need to do is resample the audio (or the features derived therefrom) to the same sample rate as the EEG (256 Hz) in order to obtain time-aligned signals. </p> <p>If you use this dataset for your research, please cite this repository as well as the following article:</p> <p>[1] M. Thornton, D. Mandic, T. Reichenbach, "Decoding of Selective Attention to Speech From Ear-EEG Recordings". <em>submitted</em>. arXiv:2401.05187.</p> <div> <div> <div></div> </div> </div>
Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embeddings
<p>These are the relevant codes for 'Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embedding'. It consists of five parts, each containing a 'ReadMe.txt' instruction file. Please read the instructions before using it. It is worth noting to remember to change the file path in the code.</p><p>Please process a complete data sample of a driver in the order of 1-5. The driving data and EEG data of each driver are separated and placed in two corresponding folders in 'driving' and 'EEG'.</p>
Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embeddings
<p>These are the relevant codes for 'Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embedding'. It consists of five parts, each containing a 'ReadMe.txt' instruction file. Please read the instructions before using it. It is worth noting to remember to change the file path in the code.</p><p> </p>
Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embeddings
<p>These are the relevant codes for 'Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embedding'. This dataset contains 14 folders. The content descriptions of these folders can be found in the 'ReadMe.txt' file, and each code file provides information about the input, output, and methods. It is important to note that the 'Data' folder is used to save the raw data, and other codes use loops to read data from the data list. There are several examples in the 'Data' folder. Please save driving data and EEG data according to the form of the examples. In addition, it also includes a compressed file that contains some of the data cases used in the test model. Please make sure to store the data in the specified format or modify the file paths accordingly when using the codes.</p>
Drive with Your Brain: Personalized Intelligent Driving with DR-EEG Decoding and Situational Embeddings
<p>This is the dataset for '<strong>Drive with Your Brain: Personalized Intelligent Driving with DR-EEG Decoding and Situational Embeddings</strong>'. This dataset mainly includes two parts: <strong>data </strong>and <strong>code</strong>.</p><p><strong>Data</strong></p><p>We used the car-following data of 133 drivers that collected through three driving simulation experiments. Detailed information and download links can be found in 'RawData. txt'.</p><p><strong>Code</strong></p><p>The main codes consist of 13 folders and a rar compressed file. A detailed description of each folder is provided in 'ReadMe. txt'. </p>
Drive with Your Brain: Personalized Intelligent Driving with DR-EEG Decoding and Situational Embeddings
<p>The implementation of the paper "Drive with Your Brain: Personalized Intelligent Driving with DR-EEG Decoding and Situational Embeddings".</p>
OASIS EEG Dataset: Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset
<p>Emotion classification using electroencephalography (EEG) data and machine learning techniques have been on the rise in the recent past. However, past studies use data from medical-grade EEG setups with long set-up times and environment constraints. The images from the OASIS image dataset were used to elicit valence and arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. We propose a novel feature ranking technique and incremental learning approach to analyze performance dependence on the number of participants. The analysis is carried out on publicly available datasets: DEAP and DREAMER for benchmarking. Leave-one-subject-out cross-validation was carried out to identify subject bias in emotion elicitation patterns. The collected dataset and pipeline are made open source. </p> <p>Code: <a href="https://github.com/rohitgarg025/Decoding_EEG">https://github.com/rohitgarg025/Decoding_EEG</a></p>
Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embeddings
<p>These are the relevant codes for 'Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embedding'. It consists of five parts, each containing a 'ReadMe.txt' instruction file. Please read the instructions before using it. It is worth noting to remember to change the file path in the code.</p>
Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embeddings
<p>These data are part of the data sample of the paper "Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embeddings". A partial sample was taken at random. This includes one compressed files: 'Processed Data and model.rar'. 'Processed data and model.rar' includes data samples, models, and code that are used for validation. A detailed description can be found in 'Read_Me.txt'.</p><p><br> </p>
Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embeddings
<p>These data are part of the data sample of the paper "Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embeddings". A partial sample was taken at random. For use by editors and reviewers. This includes one compressed files: 'RAW Data.zip'. 'RAW Data.zip' includes EEG Data and driving behavior data as well as code for extracting features and training individual models. A detailed description can be found in 'Read_Me.txt'. Video footage of the experimental process of collecting driving data and EEG data is in another link (10.5281/zenodo.8093321).</p>
Drive with Your Brain: Personalized Prediction of Driving Behaviors with DR-EEG Decoding and Situational Embeddings
<p>This is a video clip of an experiment that collected driving data and EEG data.</p>
Drive with Your Brain: Personalized Intelligent Driving with DR-EEG Decoding and Situational Embeddings
<p>This is the unprocessed data collected, including driving data and EEG data from exp2.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.